The present invention relates to automotive applications, and more specifically, to detecting vehicles that behave in an anomalous fashion.
Being able to detect vehicles that behave in an anomalous way is crucial for creating a safe driving environment. For example, occasionally there may be vehicles entering a highway from an exit, going down a one-way road in the wrong direction, driving erratically in a zig-zag line, and so on. Such behaviors can be quite dangerous not only for the driver herself, but also for any other drivers in the vicinity, and must be detected in real time, such that other drivers, pedestrians, bicyclists, etc. can be notified as quickly as possible.
One approach for detecting such an anomalous vehicle involves defining anomalous patterns as rules, that is, informing a traffic monitoring system about anomalous trajectories that are not allowed for vehicles, or defining a rule in the system for comparing the road direction and the heading of vehicles, in order to detect vehicles that are moving the wrong way. Another approach involves using sensors installed on, for example, toll gates of roads, such as ETC in Japan, or EZ-Pass in the United States, such that the sensors can detect vehicles that are moving wrong way.
However, each of these approaches each has its own disadvantages. For example, using rules essentially depends on human efforts and heuristics. Using sensors on gates can only be applied to very limited area. In real situations, however, anomalous patterns highly depend on dynamic situations that frequently change. For example, if there is an obstacle in your lane of the road, it may be appropriate to momentarily go down the wrong side of the road to avoid vehicle accident, assuming there is no oncoming traffic, of course. Human effort and heuristics to define anomalous vehicle behavior would thus need to increase in proportion to the frequency of changes of actual situations, including routes, maps, regulations, etc. Therefore, better methods are needed for detecting anomalous behavior by vehicles.